SQL SERVER → FABRIC
Modernize SQL Server into Microsoft Fabric without leaving the Microsoft estate.
Datachecks automates SQL Server assessment, OneLake target design, T-SQL translation, validation and reconciliation while experts decide the Warehouse and Lakehouse split and the capacity model.
Supported objects and automation depth vary by source, target, and migration scope.
THE MIGRATION CHALLENGE
SQL Server → Fabric looks familiar and behaves differently.
Fabric keeps T-SQL, so the migration reads as low risk. Underneath, data lands as Delta in OneLake, compute is capacity-based rather than per-server, and the Warehouse and Lakehouse split is a design decision with no SQL Server equivalent.
1
Architect-owned
Warehouse or Lakehouse
Fabric offers both, and the choice per object changes how it is written, queried and governed. There is no SQL Server equivalent to this decision, so it cannot be inherited from the source.
2
Expert review
Familiar T-SQL, different surface
Fabric Warehouse speaks T-SQL, which makes the migration look trivial. The implemented surface differs from SQL Server, and the gaps only appear once procedures are run.
3
Human-owned
Capacity, not servers
Fabric bills against shared capacity units rather than per-server licensing. Workloads that coexisted comfortably on one SQL Server instance can contend for the same capacity.
4
Automated with review
Delta under the covers
Tables land as Delta in OneLake. File layout, small-file behaviour and maintenance become real concerns for anyone used to a managed relational store.
5
Expert review
Agent jobs and pipelines
SQL Server Agent jobs encode the operational schedule and become Fabric pipelines. The dependency order has to be reconstructed rather than assumed.
6
Automated
Reporting cutover
Power BI models often sit directly on SQL Server. Reconciliation has to prove parity before semantic models are repointed, or reporting breaks quietly.
HOW DATACHECKS HELPS
Understand. Map. Translate. Validate.
The migration is executed through controlled agent workflows, with migration experts reviewing ambiguity, unsupported patterns, business rules, and critical exceptions.
01 · UNDERSTAND
Assessment + Discovery
Inventory the SQL Server estate against Fabric items.
Catalogue schemas, tables, views, procedures, functions and Agent jobs, then classify which land in a Fabric Warehouse, which belong in a Lakehouse, and which should be retired.
SQL Server estate mapped to Fabric items
02 · MAP
Source → Target Mapping
Design the OneLake target.
Map SQL Server schemas and types into Fabric tables, decide Warehouse versus Lakehouse placement, and plan shortcuts where data should be referenced rather than copied.
Reviewed Fabric target design
03 · TRANSLATE
SQL + Procedural Translation
Translate T-SQL for the Fabric surface.
Convert supported T-SQL and procedural logic to the Fabric Warehouse dialect or to Spark in a Lakehouse, and surface constructs the Fabric T-SQL surface does not implement.
Fabric-ready logic with exceptions surfaced
04 · VALIDATE
Testing + Reconciliation
Compare SQL Server and Fabric outputs.
Generate tests, compare counts and aggregates, validate transformations, and reconcile before Power BI models are repointed at the new semantic layer.
Validated Fabric outputs with reconciliation evidence
MIGRATION EVIDENCE
Every stage leaves behind something your team can review.
SOURCE → TARGET MAPPING
CustomerId UNIQUEIDENTIFIER → CustomerId VARCHAR(36)
TRANSFORMATION: TRIM + UPPER · CONFIDENCE 97% · REVIEWED
TRANSLATION
T-SQL → Fabric Warehouse T-SQL
TRANSLATED · VALIDATED
EXCEPTION
Procedure using a T-SQL feature absent from the Fabric surface
EXPERT REVIEW REQUIRED
DELIVERY TIME
Compress months of SQL Server modernization into weeks.
Automate inventory, target design, repetitive T-SQL conversion, test generation and reconciliation while experts own the Warehouse and Lakehouse split and capacity planning.
TRADITIONAL MIGRATION
MONTHS
Understand
Map
Translate
Test & validate
Reconcile
Cutover
WITH DATACHECKS
WEEKS
Understand
Map
Translate + test
Validate + reconcile
Cutover
Why the timeline shrinks: automated estate analysis · generated mappings · accelerated SQL translation · generated tests · automated reconciliation. Bar lengths are illustrative, not project commitments.
MIGRATION CONFIDENCE
Validate Fabric before repointing reporting.
Validate translated logic and migrated tables against SQL Server, reconcile critical results, and resolve exceptions before semantic models and reports move across.
TRANSLATE
TEST ✓
TARGET LOAD
VALIDATE ✓
RECONCILE ✓
EXCEPTIONS REVIEWED ✓
READY FOR CUTOVER
HUMAN IN THE LOOP
Automate the repeatable work. Keep experts on the decisions.
AGENT EXECUTION
Estate inventory · metadata analysis · profiling · standard mappings · common SQL translation · test generation · row-count checks · aggregate comparisons
EXPERT REVIEW
Ambiguous mappings · unsupported procedural patterns · complex logic · unusual transformation patterns · reconciliation discrepancies
HUMAN-OWNED
Business-rule interpretation · critical exception resolution · acceptance criteria · migration scope decisions · cutover approval
ENTERPRISE DEPLOYMENT
Run the migration engine where the data lives.
Deploy Datachecks within enterprise-controlled infrastructure, connect approved AI models, and keep migration data, metadata, execution, and evidence inside your security boundary.
Private Deployment
BYOM
SAML SSO
SOC 2
ISO 27001
RELATED RESOURCES
Go deeper on this migration.
FAQ
Frequently asked questions
Is SQL Server to Fabric a lift-and-shift because both are Microsoft?
Can our SSIS packages run in Fabric?
Should we choose Fabric Warehouse or Lakehouse?
What T-SQL features are not available in Fabric Warehouse?
How do our reports benefit from Fabric?
How is Fabric output validated against SQL Server?
PLANNING THIS MIGRATION?
Start with the estate you already have.
Share your source environment, target architecture, approximate object volumes, and migration goals. We’ll help identify complexity, scope, and where automation can remove manual delivery work.
Useful to bring: source schemas · object counts · procedural code volume · target architecture · timelines